Model Interpretability: The Conversation Continues

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Insights are drawn from Bin Yu, W....

On Being Model-driven: Metrics and Monitoring

This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model reliability, consistency and...

Data Ethics: Contesting Truth and Rearranging Power

This Domino Data Science Field Note covers Chris Wiggins's recent data ethics seminar at Berkeley. The article focuses on 1) proposed frameworks for defining and designing...

Data Science, Past & Future

Paco Nathan presented, "Data Science, Past & Future", at Rev. This blog post provides a concise session summary, a video, and a written transcript. Session Summary...

Seeking Reproducibility within Social Science: Search and Discovery

Julia Lane, NYU Professor, Economist and cofounder of the Coleridge Initiative, presented “Where’s the Data: A New Approach to Social Science Search & Discovery” at Rev....

Data Science at The New York Times

Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find...

Product Management for AI

Pete Skomoroch presented “Product Management for AI” at Rev. This post provides a distilled summary, video, and full transcript. Session Summary Pete Skomoroch’s “Product Management for...

MNIST Expanded: 50,000 New Samples Added

This post provides a distilled overview regarding the rediscovery of 50,000 samples within the MNIST dataset.  MNIST: The Potential Danger of Overfitting Recently, Chhavi Yadav (NYU)...

Machine Learning Product Management: Lessons Learned

This Domino Data Science Field Note covers Pete Skomoroch’s recent Strata London talk. It focuses on his ML product management insights and lessons learned. If you...

Addressing Irreproducibility in the Wild

This Domino Data Science Field Note provides highlights and excerpted slides from Chloe Mawer’s "The Ingredients of a Reproducible Machine Learning Model" talk at a recent...

On Collaboration Between Data Science, Product, and Engineering Teams

Eugene Mandel, Head of Product at Superconductive Health, recently dropped by Domino HQ to candidly discuss cross-team collaboration within data science. Mandel’s previous leadership roles within...

Data Science vs Engineering: Tension Points

This blog post provides highlights and a full written transcript from the panel, “Data Science Versus Engineering: Does It Really Have To Be This Way?” with...

Trust in LIME: Yes, No, Maybe So? 

In this Domino Data Science Field Note, we briefly discuss an algorithm and framework for generating explanations, LIME (Local Interpretable Model-Agnostic Explanations), that may help data...

Next page